Postdoc Fellow - Imaging Physics (Dr. Chengyue Wu's Lab)
- Requisition #: 600651-202312051114
- Department: Imaging Physics - Research
- Location: MDA-On-Site, N/A
- Posted Date: 12/5/2023
A postdoctoral fellowship position is available in the Department of Imaging Physics in the laboratory of Chengyue Wu, PhD. Dr. Chengyue Wu's research interests focus on computational precision oncology, especially integrating computational/mathematical approaches with emerging biomedical imaging techniques to improve the diagnosis, prognosis, and treatment of human cancers. Dr. Wu has extensive experience on developing and validating image processing methods and image-guided models for investigating tumor growth and treatment response, tumor-associated vasculature and microenvironment, and drug delivery. The lab is in a highly collaborative research environment with access to world-class resources, expertise, and data. Current projects seeking postdoctoral fellows include:
Image-guided computational modeling ("digital twins") to predict and optimize cancer (especially breast cancer) treatment response on a patient-specific basis.
Development of deep learning models, longitudinal image analysis, and multi-modality data integration to improve breast cancer early detection.
LEARNING OBJECTIVES
This postdoctoral fellow will engage in highly productive interdisciplinary research projects in image-guided precision oncology and personalized cancer healthcare. The fellow will expand their knowledge and skills in quantitative imaging, image analysis, artificial intelligence (AI)/deep learning technologies, mathematical biomechanical modeling, inverse problems, and uncertainty quantification. The fellow will have opportunities to contribute to ongoing research projects and will be encouraged to explore and develop new areas of research interest with guidance from the mentor. The fellow will be expected to work closely with research/clinical collaborators, communicate findings via reports, abstracts, presentations, and publications, and actively participate in seminars, conferences, and related academic endeavors.
ELIGIBILITY REQUIREMENTS
Applicants should have earned a Ph.D. in one of the natural sciences, computer sciences, applied mathematics, engineering, or related fields or a medical degree. Experience with machine learning and deep learning techniques, mathematical modeling, or medical image analysis is preferred. Applicants do not need to be US citizens or permanent residents. This appointment is not part of a clinical training program; individuals holding an M.D. degree or equivalent are not permitted to engage in patient care activity.
ADDITIONAL APPLICATION INFORMATION
The trainee will be appointed for one year from the date of hire with an option to be renewed for up to three years.
POSITION INFORMATION
MD Anderson follows the NIH stipend levels as outlined by the Kirchstein - NRSA. This full-time trainee position will provide a salary between $56,484 to $68,604, dependent upon the years of postgraduate experience.
MD Anderson offers compensated trainees:
- Paid medical benefits (zero premium) starting on first day for trainees who work 30 or more hours per week
- Group Dental, Vision, Life, AD&D and Disability coverage
- Paid Education Vacation and Sick Leave
- Paid institutional holidays, wellness leave, childcare leave and other paid leave programs
- Teachers Retirement System defined-benefit pension plan and two voluntary retirement plans
- Employer paid life, AD&D and an illness-related reduced salary pay program
- Health Savings Account and Dependent Care Reimbursement flexible spending accounts
- Fertility benefits
- State of Texas longevity pay
- Extensive wellness, fitness, employee health programs and employee resource groups
Image-guided computational modeling ("digital twins") to predict and optimize cancer (especially breast cancer) treatment response on a patient-specific basis.
Development of deep learning models, longitudinal image analysis, and multi-modality data integration to improve breast cancer early detection.
LEARNING OBJECTIVES
This postdoctoral fellow will engage in highly productive interdisciplinary research projects in image-guided precision oncology and personalized cancer healthcare. The fellow will expand their knowledge and skills in quantitative imaging, image analysis, artificial intelligence (AI)/deep learning technologies, mathematical biomechanical modeling, inverse problems, and uncertainty quantification. The fellow will have opportunities to contribute to ongoing research projects and will be encouraged to explore and develop new areas of research interest with guidance from the mentor. The fellow will be expected to work closely with research/clinical collaborators, communicate findings via reports, abstracts, presentations, and publications, and actively participate in seminars, conferences, and related academic endeavors.
ELIGIBILITY REQUIREMENTS
Applicants should have earned a Ph.D. in one of the natural sciences, computer sciences, applied mathematics, engineering, or related fields or a medical degree. Experience with machine learning and deep learning techniques, mathematical modeling, or medical image analysis is preferred. Applicants do not need to be US citizens or permanent residents. This appointment is not part of a clinical training program; individuals holding an M.D. degree or equivalent are not permitted to engage in patient care activity.
ADDITIONAL APPLICATION INFORMATION
The trainee will be appointed for one year from the date of hire with an option to be renewed for up to three years.
POSITION INFORMATION
MD Anderson follows the NIH stipend levels as outlined by the Kirchstein - NRSA. This full-time trainee position will provide a salary between $56,484 to $68,604, dependent upon the years of postgraduate experience.
MD Anderson offers compensated trainees:
- Paid medical benefits (zero premium) starting on first day for trainees who work 30 or more hours per week
- Group Dental, Vision, Life, AD&D and Disability coverage
- Paid Education Vacation and Sick Leave
- Paid institutional holidays, wellness leave, childcare leave and other paid leave programs
- Teachers Retirement System defined-benefit pension plan and two voluntary retirement plans
- Employer paid life, AD&D and an illness-related reduced salary pay program
- Health Savings Account and Dependent Care Reimbursement flexible spending accounts
- Fertility benefits
- State of Texas longevity pay
- Extensive wellness, fitness, employee health programs and employee resource groups
FACULTY MENTOR
Dr. Chengyue Wu
